import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
   from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments




@require_torch
class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 def 		__UpperCAmelCase				(		self				:   Optional[int]							, lowercase_				:   Union[str, Any])						->	Union[str, Any]:


   """simple docstring"""







   for model_result in results.values():
     for batch_size, sequence_length in zip(model_result["bs"]							, model_result["ss"]):
       _UpperCamelCase           =    model_result["result"][batch_size][sequence_length]
       self.assertIsNotNone(lowercase_)







 def 		__UpperCAmelCase				(		self				:   List[str])						->	List[str]:


   """simple docstring"""







   _UpperCamelCase           =    "sshleifer/tiny-gpt2"
   _UpperCamelCase           =    PyTorchBenchmarkArguments(
       models=[MODEL_ID]							, training=lowercase_							, inference=lowercase_							, sequence_lengths=[8]							, batch_sizes=[1]							, multi_process=lowercase_							, )
   _UpperCamelCase           =    PyTorchBenchmark(lowercase_)
   _UpperCamelCase           =    benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result)
   self.check_results_dict_not_empty(results.memory_inference_result)







 def 		__UpperCAmelCase				(		self				:   Any)						->	str:


   """simple docstring"""







   _UpperCamelCase           =    "sgugger/tiny-distilbert-classification"
   _UpperCamelCase           =    PyTorchBenchmarkArguments(
       models=[MODEL_ID]							, training=lowercase_							, inference=lowercase_							, sequence_lengths=[8]							, batch_sizes=[1]							, multi_process=lowercase_							, only_pretrain_model=lowercase_							, )
   _UpperCamelCase           =    PyTorchBenchmark(lowercase_)
   _UpperCamelCase           =    benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result)
   self.check_results_dict_not_empty(results.memory_inference_result)







 def 		__UpperCAmelCase				(		self				:   List[str])						->	str:


   """simple docstring"""







   _UpperCamelCase           =    "sshleifer/tiny-gpt2"
   _UpperCamelCase           =    PyTorchBenchmarkArguments(
       models=[MODEL_ID]							, training=lowercase_							, inference=lowercase_							, torchscript=lowercase_							, sequence_lengths=[8]							, batch_sizes=[1]							, multi_process=lowercase_							, )
   _UpperCamelCase           =    PyTorchBenchmark(lowercase_)
   _UpperCamelCase           =    benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result)
   self.check_results_dict_not_empty(results.memory_inference_result)







 @unittest.skipIf(torch_device == "cpu"							, "Cant do half precision")
 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	List[str]:


   """simple docstring"""







   _UpperCamelCase           =    "sshleifer/tiny-gpt2"
   _UpperCamelCase           =    PyTorchBenchmarkArguments(
       models=[MODEL_ID]							, training=lowercase_							, inference=lowercase_							, fpaa=lowercase_							, sequence_lengths=[8]							, batch_sizes=[1]							, multi_process=lowercase_							, )
   _UpperCamelCase           =    PyTorchBenchmark(lowercase_)
   _UpperCamelCase           =    benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result)
   self.check_results_dict_not_empty(results.memory_inference_result)







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    "sshleifer/tiny-gpt2"
   _UpperCamelCase           =    AutoConfig.from_pretrained(lowercase_)
   # set architectures equal to `None`
   _UpperCamelCase           =    None
   _UpperCamelCase           =    PyTorchBenchmarkArguments(
       models=[MODEL_ID]							, training=lowercase_							, inference=lowercase_							, sequence_lengths=[8]							, batch_sizes=[1]							, multi_process=lowercase_							, )
   _UpperCamelCase           =    PyTorchBenchmark(lowercase_							, configs=[config])
   _UpperCamelCase           =    benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result)
   self.check_results_dict_not_empty(results.memory_inference_result)







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	Optional[int]:


   """simple docstring"""







   _UpperCamelCase           =    "sshleifer/tiny-gpt2"
   _UpperCamelCase           =    PyTorchBenchmarkArguments(
       models=[MODEL_ID]							, training=lowercase_							, inference=lowercase_							, sequence_lengths=[8]							, batch_sizes=[1]							, multi_process=lowercase_							, )
   _UpperCamelCase           =    PyTorchBenchmark(lowercase_)
   _UpperCamelCase           =    benchmark.run()
   self.check_results_dict_not_empty(results.time_train_result)
   self.check_results_dict_not_empty(results.memory_train_result)







 @unittest.skipIf(torch_device == "cpu"							, "Can't do half precision")
 def 		__UpperCAmelCase				(		self				:   int)						->	List[str]:


   """simple docstring"""







   _UpperCamelCase           =    "sshleifer/tiny-gpt2"
   _UpperCamelCase           =    PyTorchBenchmarkArguments(
       models=[MODEL_ID]							, training=lowercase_							, inference=lowercase_							, sequence_lengths=[8]							, batch_sizes=[1]							, fpaa=lowercase_							, multi_process=lowercase_							, )
   _UpperCamelCase           =    PyTorchBenchmark(lowercase_)
   _UpperCamelCase           =    benchmark.run()
   self.check_results_dict_not_empty(results.time_train_result)
   self.check_results_dict_not_empty(results.memory_train_result)







 def 		__UpperCAmelCase				(		self				:   str)						->	List[Any]:


   """simple docstring"""







   _UpperCamelCase           =    "sshleifer/tiny-gpt2"
   _UpperCamelCase           =    AutoConfig.from_pretrained(lowercase_)
   _UpperCamelCase           =    PyTorchBenchmarkArguments(
       models=[MODEL_ID]							, training=lowercase_							, inference=lowercase_							, sequence_lengths=[8]							, batch_sizes=[1]							, multi_process=lowercase_							, )
   _UpperCamelCase           =    PyTorchBenchmark(lowercase_							, configs=[config])
   _UpperCamelCase           =    benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result)
   self.check_results_dict_not_empty(results.memory_inference_result)







 def 		__UpperCAmelCase				(		self				:   List[str])						->	Union[str, Any]:


   """simple docstring"""







   _UpperCamelCase           =    "sshleifer/tinier_bart"
   _UpperCamelCase           =    AutoConfig.from_pretrained(lowercase_)
   _UpperCamelCase           =    PyTorchBenchmarkArguments(
       models=[MODEL_ID]							, training=lowercase_							, inference=lowercase_							, sequence_lengths=[8]							, batch_sizes=[1]							, multi_process=lowercase_							, )
   _UpperCamelCase           =    PyTorchBenchmark(lowercase_							, configs=[config])
   _UpperCamelCase           =    benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result)
   self.check_results_dict_not_empty(results.memory_inference_result)







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	Any:


   """simple docstring"""







   _UpperCamelCase           =    "sshleifer/tiny-gpt2"
   _UpperCamelCase           =    AutoConfig.from_pretrained(lowercase_)
   _UpperCamelCase           =    PyTorchBenchmarkArguments(
       models=[MODEL_ID]							, training=lowercase_							, inference=lowercase_							, sequence_lengths=[8]							, batch_sizes=[1]							, multi_process=lowercase_							, )
   _UpperCamelCase           =    PyTorchBenchmark(lowercase_							, configs=[config])
   _UpperCamelCase           =    benchmark.run()
   self.check_results_dict_not_empty(results.time_train_result)
   self.check_results_dict_not_empty(results.memory_train_result)







 def 		__UpperCAmelCase				(		self				:   Union[str, Any])						->	Dict:


   """simple docstring"""







   _UpperCamelCase           =    "sshleifer/tinier_bart"
   _UpperCamelCase           =    AutoConfig.from_pretrained(lowercase_)
   _UpperCamelCase           =    PyTorchBenchmarkArguments(
       models=[MODEL_ID]							, training=lowercase_							, inference=lowercase_							, sequence_lengths=[8]							, batch_sizes=[1]							, multi_process=lowercase_							, )
   _UpperCamelCase           =    PyTorchBenchmark(lowercase_							, configs=[config])
   _UpperCamelCase           =    benchmark.run()
   self.check_results_dict_not_empty(results.time_train_result)
   self.check_results_dict_not_empty(results.memory_train_result)







 def 		__UpperCAmelCase				(		self				:   str)						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    "sshleifer/tiny-gpt2"
   with tempfile.TemporaryDirectory() as tmp_dir:
     _UpperCamelCase           =    PyTorchBenchmarkArguments(
         models=[MODEL_ID]							, training=lowercase_							, inference=lowercase_							, save_to_csv=lowercase_							, sequence_lengths=[8]							, batch_sizes=[1]							, inference_time_csv_file=os.path.join(lowercase_							, "inf_time.csv")							, train_memory_csv_file=os.path.join(lowercase_							, "train_mem.csv")							, inference_memory_csv_file=os.path.join(lowercase_							, "inf_mem.csv")							, train_time_csv_file=os.path.join(lowercase_							, "train_time.csv")							, env_info_csv_file=os.path.join(lowercase_							, "env.csv")							, multi_process=lowercase_							, )
     _UpperCamelCase           =    PyTorchBenchmark(lowercase_)
     benchmark.run()
     self.assertTrue(Path(os.path.join(lowercase_							, "inf_time.csv")).exists())
     self.assertTrue(Path(os.path.join(lowercase_							, "train_time.csv")).exists())
     self.assertTrue(Path(os.path.join(lowercase_							, "inf_mem.csv")).exists())
     self.assertTrue(Path(os.path.join(lowercase_							, "train_mem.csv")).exists())
     self.assertTrue(Path(os.path.join(lowercase_							, "env.csv")).exists())







 def 		__UpperCAmelCase				(		self				:   Any)						->	str:


   """simple docstring"""







   _UpperCamelCase           =    "sshleifer/tiny-gpt2"

   def _check_summary_is_not_empty(lowercase_				:   Optional[int]):
     self.assertTrue(hasattr(lowercase_							, "sequential"))
     self.assertTrue(hasattr(lowercase_							, "cumulative"))
     self.assertTrue(hasattr(lowercase_							, "current"))
     self.assertTrue(hasattr(lowercase_							, "total"))

   with tempfile.TemporaryDirectory() as tmp_dir:
     _UpperCamelCase           =    PyTorchBenchmarkArguments(
         models=[MODEL_ID]							, training=lowercase_							, inference=lowercase_							, sequence_lengths=[8]							, batch_sizes=[1]							, log_filename=os.path.join(lowercase_							, "log.txt")							, log_print=lowercase_							, trace_memory_line_by_line=lowercase_							, multi_process=lowercase_							, )
     _UpperCamelCase           =    PyTorchBenchmark(lowercase_)
     _UpperCamelCase           =    benchmark.run()
     _check_summary_is_not_empty(result.inference_summary)
     _check_summary_is_not_empty(result.train_summary)
     self.assertTrue(Path(os.path.join(lowercase_							, "log.txt")).exists())





import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


lowerCamelCase__  =					get_logger(__name__)




class 					_UpperCAmelCase			(     enum.Enum  ):



 '''simple docstring'''




 __A			       =					'''all_checks'''
 __A			       =					'''basic_checks'''
 __A			       =					'''no_checks'''




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''






def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__=None )      ->int:




  '''simple docstring'''

  if expected_checksums is None:
    logger.info("Unable to verify checksums." )
    return
  if len(set(a__ ) - set(a__ ) ) > 0:
    raise ExpectedMoreDownloadedFiles(str(set(a__ ) - set(a__ ) ) )
  if len(set(a__ ) - set(a__ ) ) > 0:
    raise UnexpectedDownloadedFile(str(set(a__ ) - set(a__ ) ) )
  _UpperCamelCase           =    [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
  _UpperCamelCase           =    " for " + verification_name if verification_name is not None else ""
  if len(a__ ) > 0:
    raise NonMatchingChecksumError(
        f'Checksums didn\'t match{for_verification_name}:\n'
        f'{bad_urls}\n'
        "Set `verification_mode='no_checks'` to skip checksums verification and ignore this error" )
  logger.info("All the checksums matched successfully" + for_verification_name )




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''






def 				lowerCAmelCase__       (			a__    ,     a__ )      ->Dict:




  '''simple docstring'''

  if expected_splits is None:
    logger.info("Unable to verify splits sizes." )
    return
  if len(set(a__ ) - set(a__ ) ) > 0:
    raise ExpectedMoreSplits(str(set(a__ ) - set(a__ ) ) )
  if len(set(a__ ) - set(a__ ) ) > 0:
    raise UnexpectedSplits(str(set(a__ ) - set(a__ ) ) )
  _UpperCamelCase           =    [
      {"expected": expected_splits[name], "recorded": recorded_splits[name]}
      for name in expected_splits
      if expected_splits[name].num_examples != recorded_splits[name].num_examples
  ]
  if len(a__ ) > 0:
    raise NonMatchingSplitsSizesError(str(a__ ) )
  logger.info("All the splits matched successfully." )


def 				lowerCAmelCase__       (			a__    ,     a__ = True )      ->dict:




  '''simple docstring'''

  if record_checksum:
    _UpperCamelCase           =    shaaaa()
    with open(a__    ,     "rb" ) as f:
      for chunk in iter(lambda: f.read(1 << 20 )    ,     B"" ):
        m.update(a__ )
      _UpperCamelCase           =    m.hexdigest()
  else:
    _UpperCamelCase           =    None
  return {"num_bytes": os.path.getsize(a__ ), "checksum": checksum}







def 				lowerCAmelCase__       (			a__ )      ->Any:




  '''simple docstring'''

  if dataset_size and config.IN_MEMORY_MAX_SIZE:
    return dataset_size < config.IN_MEMORY_MAX_SIZE
  else:
    return False




